System and method for predicting future disk failures

ABSTRACT

According to one embodiment, a diagnostic parameter of a target storage disk of a storage system is received. A first weight factor is determined from a first quantile distribution representation based on a value of the diagnostic parameter, where the first quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known failed disks. A second weight factor is determined from a second quantile distribution representation based on the value of the diagnostic parameter, where the second quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known working disks. A third weight factor is determined for the diagnostic parameter for the target storage disk based on the first weight factor and the second weight factor and a probability of potential disk failure of the target storage disk is determined based on the third weight factor.

RELATED APPLICATIONS

This application is a continuation-in-part (CIP) of co-pending U.S.patent application Ser. No. 14/037,199, filed Sep. 25, 2013, which isincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to data storagesystems. More particularly, embodiments of the invention relate topredicting future disk failures.

BACKGROUND

Data storage utilization is continually increasing, causing theproliferation of storage systems in data centers. Monitoring andmanaging these systems require increasing amounts of human resources.Information technology (IT) organizations often operate reactively,taking action only when systems reach capacity or fail, at which pointperformance degradation or failure has already occurred. Hard diskfailures fall into one of two basic classes: predictable failures andunpredictable failures. Predictable failures result from slow processessuch as mechanical wear and gradual degradation of storage surfaces.Monitoring can determine when such failures are becoming more likely.Unpredictable failures happen suddenly and without warning. They rangefrom electronic components becoming defective to a sudden mechanicalfailure (perhaps due to improper handling).

Self-Monitoring, Analysis and Reporting Technology (S.M.A.R.T., orsimply written as SMART) is a monitoring system for computer hard diskdrives to detect and report on various indicators of reliability, in thehope of anticipating failures. When a failure is anticipated byS.M.A.R.T., the user may choose to replace the drive to avoid unexpectedoutage and data loss. The manufacturer may be able to use the S.M.A.R.T.data to discover where faults lie and prevent them from recurring infuture drive designs. However, not all of the S.M.A.R.T. attributes canconsistently provide reliable indications of possible disk failures. TheS.M.A.R.T. attributes tend to vary and they may have differentinterpretation from one hard disk vendor or configuration to another.There has been a lack of reliable mechanism to determine which of theS.M.A.R.T. attributes to be the best disk failure indicator, as well asthe efficient ways to predict single disk failure or multi-disk failuresin a redundant array independent disks (RAID) environment. Further priortechniques do a poor job monitoring the RAID overall health status. Thediscrepancy between a working disk and impending failure is not obvious.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a storage system according to oneembodiment of the invention.

FIG. 2 is a flow diagram illustrating a method for predicting diskfailures according to one embodiment of the invention.

FIG. 3 is a diagram illustrating certain quantile distributionrepresentations which may be used with an embodiment of the invention.

FIG. 4 is a flow diagram illustrating a method for determining a diskfailure indicator according to one embodiment of the invention.

FIG. 5 is a flow diagram illustrating a method for predicting diskfailures of a single disk according to one embodiment of the invention.

FIG. 6 is a diagram illustrating a process for determining an optimalthreshold for predicting disk failures according to one embodiment ofthe invention.

FIG. 7 is a flow diagram illustrating a method for determining anoptimal threshold for predicting disk failures according to oneembodiment of the invention.

FIGS. 8A and 8B are diagrams illustrating a process for predictingmulti-disk failures according to one embodiment of the invention.

FIG. 9 is a flow diagram illustrating a method for predicting multipledisk failures according to one embodiment of the invention.

FIG. 10 is a block diagram illustrating a deduplicated storage systemaccording to one embodiment of the invention.

FIG. 11 is a diagram illustrating a process for predicting disk failuresusing quantile distribution techniques according to one embodiment ofthe invention.

FIG. 12 is a flow diagram illustrating a method for predicting diskfailures according to one embodiment of the invention.

FIG. 13 is a flow diagram illustrating a method for predicting diskfailures according to another embodiment of the invention.

DETAILED DESCRIPTION

Various embodiments and aspects of the inventions will be described withreference to details discussed below, and the accompanying drawings willillustrate the various embodiments. The following description anddrawings are illustrative of the invention and are not to be construedas limiting the invention. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentinvention. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present inventions.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to one aspect of the invention, a method using quantiledistribution techniques is provided to select one or more of diagnosticparameters, such as S.M.A.R.T. attributes and/or small computer systeminterface (SCSI) disk return codes, that are collected from the disks tobe the most reliable disk failure indicator or indicators for a givencertain storage configuration or environment (e.g., target storagesystem or disks). According to one embodiment, diagnostic parameters arecollected from a set of historically known failed disks and knownworking disks of a particular storage system or configuration (e.g., astorage system or systems deployed at a particular enterprise ororganization). The diagnostic parameters of the known failed disks andknown working disks are analyzed or trained in view each other todetermine which of the those diagnostic parameters can be used to mostefficiently or consistently distinguish or identify a failed disk orworking disk from the set of disks.

According to one embodiment, for each of the diagnostic parameters, afirst quantile distribution representation and a second quantiledistribution representation (e.g., quantile distribution graphs orcurves) are generated for the known failed disks and the known workdisks, respectively. For each of the diagnostic parameters, the firstand second quantile distribution representations are analyzed todetermine its maximum difference value between the first and secondquantile distribution representations. The maximum difference values ofall the diagnostic parameters are then compared to each other to selectone or more of the diagnostic parameters that are largest amongst all orabove certain predetermined threshold as one or more disk failureindicators, which can consistently identify a potential failed disk froma group of disks.

According to another aspect of the invention, the selected disk failureindicator may be used to predict the disk failure probability of aparticular disk. According to one embodiment, values of a predetermineddiagnostic parameter (representing the selected disk failure indicator,such as the reallocated sector count as part of S.M.A.R.T. attributes orthe medium error as part of SCSI disk return codes) are collected from aset of known failed disks and known working disks associated with atarget storage system. A first and second quantile distribution graphsof the values of the predetermined diagnostic parameter for the knownfailed disks and known working disks are generated, respectively, inview of a range of values of the known failed disks. An optimalthreshold of the values of the predetermined diagnostic parameter isdetermined by comparing the first and second quantile distributiongraphs. In one embodiment, the optimal threshold is identified at aposition where the difference between the first and second quantiledistribution graphs reaches the maximum. The optimal threshold is thenutilized to indicate or predict whether a particular target disk mayhave a higher probability of disk failure, for example, by comparing thevalue of the predetermined diagnostic obtained from the target diskagainst the optimal threshold.

According to another aspect of the invention, the selected disk failureindicator may be used to predict the disk failure probability ofmultiple disks in a RAID configuration. According to one embodiment,values of a predetermined diagnostic parameter (representing theselected disk failure indicator, such as the reallocated sector count aspart of S.M.A.R.T. attributes) are collected from a set of known faileddisks associated with a target storage system. A quantile distributiongraph is generated for the values in view of percentiles (e.g., 10%,20%, . . . , 100%) of a number of known failed disks involved in thequantile distribution. Subsequently, when values of the predetermineddiagnostic parameter are collected from a set of target storage disks,the collected values are applied to the quantile distribution graph todetermine their respective percentiles of the target disks. Eachpercentile represents a probability of the corresponding disk failure.The individual disk failure probabilities are then used to calculate theprobability of disk failures of two or more of the target disks.

According to another aspect of the invention, in addition to thestraightforward approach that uses the values of certain diagnosticparameters, a weight vector algorithm is utilized that takes intoaccount all errors to recognize soon-to-fail disks. The weight vectoralgorithm evaluates the error degree of every disk fault metric, assignsweights accordingly, and consolidates all weights with Euclidean lengthto present the failure probability. In particular, a disk is providedwith a weight or score indicating different error levels instead of onlypredicting whether the disk will fail. The weight or score enables thesystem to adjust a failure threshold by monitoring every disk's score inthe same RAID group. For example, if there are multiple disks withcomparatively high scores indicating that they may be simultaneouslyapproaching their end of lifespan, the threshold used to categorizewhether a particular disk may fail may be lowered to fail the disks moreaggressively, which may reduce the probability of potential multipledisk failures and improve the overall system reliability.

Specifically, according to one embodiment of the invention, when a firstand a second quantile distribution representations are createdrepresenting a set of known failed disks and a set of known workingdisks, respectively, a default weight factor or score is assigned toeach of the deciles (or intervals) evenly distributed over the rangerepresented by the quantile distribution representations. A defaultweight or score of a particular decile represents a degree of potentialdisk failure of a particular disk when the value of the diagnosticparameter of that particular disk falls within the corresponding decile.Subsequently, when a diagnostic parameter (e.g., reallocated sectorcount) of a target storage disk is received for the purpose ofpredicting disk failures of the target storage disk, a lookup operationis performed on both of the first quantile distribution representation(e.g., failed disk quantile distribution) and the second quantiledistribution representation (e.g., working disk quantile distribution)based on the value of the diagnostic parameter to determine thediagnostic parameter having that particular value is represented by boththe first and second quantile distribution representations (e.g.,whether a quantile distribution contains the value of the diagnosticparameter).

In one embodiment, if the value of the diagnostic parameter isrepresented by both the first and second quantile distributionrepresentations, a first weight factor is determined from the firstquantile distribution representation based on the value of thediagnostic parameter and a second weight factor is determined fromsecond quantile distribution representation based on the value of thediagnostic parameter. A third weight factor is then determined based onthe first and second weight factors and a disk failure probability isthen determined based on the third weight factor. If the value of thediagnostic parameter is only represented by the first quantiledistribution representation (and not by the second quantile distributionrepresentation), the first weight factor is utilized as the third weightfactor.

FIG. 1 is a block diagram illustrating a storage system according to oneembodiment of the invention. Referring to FIG. 1, system 100 includes,but is not limited to, one or more client systems 101-102communicatively coupled to one or more storage systems 104 over network103. Clients 101-102 may be any type of clients such as a server, apersonal computer (e.g., desktops, laptops, and tablets), a “thin”client, a personal digital assistant (PDA), a Web enabled appliance, agaming device, a media player, or a mobile phone (e.g., Smartphone),etc. Network 103 may be any type of networks such as a local areanetwork (LAN), a wide area network (WAN) such as Internet, or acombination thereof.

Storage system 104 may include any type of server or cluster of servers.For example, storage system 104 may be a storage server used for any ofvarious different purposes, such as to provide multiple users withaccess to shared data and/or to back up mission critical data. In oneembodiment, storage system 104 includes, but is not limited to, backupengine 106, optional deduplication storage engine 107, and one or morestorage units 108-109 communicatively coupled to each other. Storageunits 108-109 may be implemented locally (e.g., single node operatingenvironment) or remotely (e.g., multi-node operating environment) viainterconnect 120, which may be a bus and/or a network.

In response to a data file to be stored in storage units 108-109,deduplication storage engine 107 is configured to segment the data fileinto multiple chunks (also referred to as segments) according to avariety of segmentation policies or rules. Deduplication storage engine107 may choose not to store a chunk in a storage unit if the chunk hasbeen previously stored in the storage unit. In the event thatdeduplication storage engine 107 chooses not to store the chunk in thestorage unit, it stores metadata enabling the reconstruction of the fileusing the previously stored chunk. As a result, chunks of data files arestored in a deduplicated manner, either within each of storage units108-109 or across at least some of storage units 108-109. The metadata,such as metadata 110-111, may be stored in at least some of storageunits 108-109, such that files can be accessed independent of anotherstorage unit. Metadata of each storage unit includes enough informationto provide access to the files it contains.

According to one embodiment, storage system 104 further includes anoperation manager 105 to manage and monitor operations performed bystorage system 104, including periodically collecting and transmittingoperating diagnostic data to a remote device such as management system150 over network 103. In this example as shown in FIG. 1, storage system104 may be located at a client site and utilized by a client such as anenterprise or corporation, where the storage system 104 may be providedby a storage provider or vendor such as EMC® Corporation. In oneembodiment, operation manager 105 periodically collects operatingstatistics concerning operations of storage units 108-109 and transmitsdiagnostic data representing at least some of the operating statisticsto management system 150, where management system 150 is associated witha storage provider or vendor that provides storage system 104 to aclient. For example, management system 150 may be operated or owned bythe storage provider or alternatively, it may be operated by athird-party vendor on behalf of the storage provider. In one embodiment,the diagnostic data may include diagnostic parameters such as thosedefined by S.M.A.R.T. specification and/or those defined as part of theSCSI disk return codes, which may be collected from the storage system104. For example, operation manager 105 may include or communicate withan S.M.A.R.T. tool or software configured to monitor operations ofstorage units 108-109. Each of the storage units 108-109 may beimplemented one or more individual disks or alternatively, a RAID arrayof disks.

Note that storage system 104 may represent a group or cluster ofindividual storage systems, where operation manager 105 of each storagesystem may be equipped with a “phone-home” functionality that mayperiodically transmit operating status of the respective storage system,including the diagnostic parameters (e.g., S.M.A.R.T. attributes andSCSI return codes) of the associated storage disks, to a centralized ordistributed entity, such as management server 150 or dedicated datacollection entity 160 (e.g., a third-party data collection agent).

According to one embodiment, management system 150 includes a datacollector 151, disk failure predictor 152, and analysis module 153. Datacollector 151 is employed to communicate with operation manager 105 ofstorage system(s) 104 to collect diagnostic data concerning operatingstatuses of storage units 108-109, as well as storage system 104 ingeneral. Note that although one storage system is shown in FIG. 1, datacollector 151 may communicate with multiple operation managers ofmultiple storage systems to collect diagnostic data concerning therespective storage systems, which may be located at the same ordifferent geographical locations (e.g., same or different client sites).For example, management system 150 may be a centralized managementserver or cluster of servers (e.g., in the cloud) for single or multipleclients or customers.

The collected diagnostic data is stored in a storage device as part ofdiagnostic logs 154. In one embodiment, diagnostic data 154 includesdiagnostic parameters collected from various storage systems such asstorage system 104. The diagnostic parameters may be those attributes(e.g., reallocated sector, pending sector, uncorrectable sector, etc.)defined by S.M.A.R.T. Alternatively, diagnostic parameters may be thosefrom the SCSI return codes (e.g., medium error, timeout, connectionerror, data error, etc.). In one embodiment, analysis module 153 is toperform an analysis on the diagnostic data 154 such as determining whichof the diagnostic parameters can be used as the best disk failureindicator(s). Disk failure predictor 152 is configured to predict, usingthe disk failure indicator(s), which one or more of the disks of storageunits 108-109 of storage system 104 have a higher probability of diskfailures.

According to one embodiment of the invention, analysis module 153utilizes quantile distribution techniques to select one or more ofdiagnostic parameters, such as S.M.A.R.T. attributes, SCSI return codes,that are collected from the disks of storage units 108-109 to be themost reliable disk failure indicator or indicators for a given certainstorage configuration or environment (e.g., target storage system ordisks). According to one embodiment, diagnostic parameters arecollected, for example, by data collector 151 or server 160, from a setof historically known failed disks and known working disks of aparticular storage system or configuration (e.g., a storage system orsystems deployed at a particular enterprise or organization). Thediagnostic parameters of the known failed disks and known working disksare analyzed or trained by analysis module 153 in view each other todetermine which of the those diagnostic parameters can be used to mostefficiently or consistently distinguish or identify a failed disk orworking disk from the set of disks.

According to one embodiment, for each of the diagnostic parameters, afirst quantile distribution representation and a second quantiledistribution representation (e.g., quantile distribution graphs orcurves) are generated for the known failed disks and the known workdisks, respectively. For each of the diagnostic parameters, the firstand second quantile distribution representations are analyzed todetermine its maximum difference value between the first and secondquantile distribution representations. The maximum difference values ofall the diagnostic parameters are then compared to each other to selectone or more of the diagnostic parameters that are largest amongst all orabove certain predetermined threshold as one or more disk failureindicators, which can consistently identify a potential failed disk froma group of disks.

According to another embodiment of the invention, the selected diskfailure indicator may be used by disk failure predictor 152 to predictthe disk failure probability of a particular disk. According to oneembodiment, values of a predetermined diagnostic parameter (representingthe selected disk failure indicator, such as the reallocated sectorcount as part of S.M.A.R.T. attributes) are collected (e.g., bycollector 151 or server 160) from a set of known failed disks and knownworking disks associated with a target storage system. A first andsecond quantile distribution graphs of the values of the predetermineddiagnostic parameter for the known failed disks and known working disksare generated, respectively, in view of a range of values of the knownfailed disks. An optimal threshold of the value of the predetermineddiagnostic parameter is determined by comparing the first and secondquantile distribution graphs. In one embodiment, the optimal thresholdis identified at a position where the difference between the first andsecond quantile distribution graphs reaches the maximum. The optimalthreshold is then utilized by disk failure predictor 152 to indicate orpredict whether a particular target disk may have a higher probabilityof disk failure, for example, by comparing the value of thepredetermined diagnostic obtained from the target disk against theoptimal threshold.

According to a further embodiment of the invention, the selected diskfailure indicator may be used to predict the disk failure probability ofmultiple disks in a RAID configuration. According to one embodiment,values of a predetermined diagnostic parameter (representing theselected disk failure indicator, such as the reallocated sector count aspart of S.M.A.R.T. attributes) are collected (e.g., by data collector151 or server 160) from a set of known failed disks associated with atarget storage system. A quantile distribution graph is generated (e.g.,by analysis module 153 or disk failure predictor 152) for the values inview of percentiles (e.g., 10%, 20%, . . . , 100%) of a number of knownfailed disks involved in the quantile distribution. Subsequently, whenvalues of the predetermined diagnostic parameter are collected from aset of target storage disks, the collected values are applied (e.g., bydisk failure predictor 152) to the quantile distribution graph todetermine their respective percentiles of the target disks. Eachpercentile represents a probability of the corresponding disk failure.The individual disk failure probabilities are then used (e.g., by diskfailure predictor 152) to calculate the probability of disk failures oftwo or more of the target disks.

According to another embodiment of the invention, in addition to thestraightforward approach that uses the values of certain diagnosticparameters, a weight vector algorithm is utilized that takes intoaccount all errors to recognize soon-to-fail disks. The weight vectoralgorithm evaluates the error degree of every disk fault metric, assignsweights accordingly, and consolidates all weights, for example, usingEuclidean length, to present the failure probability. In particular, adisk is provided with a weight or score indicating different errorlevels instead of only predicting whether the disk will fail. The weightor score enables the system to adjust a failure threshold by monitoringevery disk's score in the same RAID group. For example, if there aremultiple disks with comparatively high scores indicating that they maybe simultaneously approaching their end of lifespan, the threshold usedto categorize whether a particular disk may fail may be lowered to failthe disks more aggressively, which may reduce the probability ofpotential multiple disk failures and improve the overall systemreliability.

Specifically, according to one embodiment of the invention, when a firstand a second quantile distribution representations are created, byanalysis module 153, representing a set of known failed disks and a setof known working disks, respectively, a default weight factor or scoreis assigned to each of the deciles (or intervals such as percentiles)evenly distributed over the range represented by the quantiledistribution representations. A default weight or score of a particulardecile represents a degree of potential disk failure of a particulardisk when the value of the diagnostic parameter of that particular diskfalls within the corresponding decile. Subsequently, when a diagnosticparameter (e.g., reallocated sector count) of a target storage disk isreceived for the purpose of predicting disk failures of the targetstorage disk, disk failure predictor 152 performs a lookup operation onboth of the first quantile distribution representation (e.g., faileddisk quartile distribution) and the second quantile distributionrepresentation (e.g., working disk quantile distribution) correspondingto the diagnostic parameter based on the value of the diagnosticparameter to determine the diagnostic parameter having that particularvalue is represented by both the first and second quantile distributionrepresentations (e.g., whether a quantile distribution contains thevalue of the diagnostic parameter).

In one embodiment, if the value of the diagnostic parameter isrepresented by both the first and second quantile distributionrepresentations (e.g., the particular value of the diagnostic parameterin question is found in both quantile distribution representations),disk failure predictor 152 determines a first weight factor from thefirst quantile distribution representation based on the value of thediagnostic parameter and determines a second weight factor from secondquantile distribution representation based on the value of thediagnostic parameter. A third weight factor is then determined based onthe first and second weight factors and a disk failure probability isthen determined based on the third weight factor. If the value of thediagnostic parameter is only represented by the first quantiledistribution representation (and not by the second quantile distributionrepresentation), the first weight factor is utilized as the third weightfactor.

FIG. 2 is a flow diagram illustrating a method for predicting diskfailures according to one embodiment of the invention. Method 200 may beperformed by processing logic which may include software, hardware, or acombination thereof. For example, method 200 may be performed bymanagement system 150 of FIG. 1. Referring to FIG. 2, at block 201,processing logic collects diagnostic parameters (e.g., at least aportion of S.M.A.R.T. attributes) from a set of historically knownfailed disks and known working disks of one or more storage systems(e.g., deduplicated backup storage systems). At block 202, processinglogic performs an analysis on the collected diagnostic parameters toidentify one or more of the diagnostic parameters as one or more diskfailure indicators that can be used to consistently or reliably indicatefuture disk failures. In one embodiment, processing logic utilizesquantile distribution techniques to select one or more of the diagnosticparameters as one or more disk failure indicators. At block 203,processing logic monitors and collects diagnostic parameters from one ormore target disks of a target storage system. At block 204, processinglogic predicts, using the selected disk failure indicator(s), theprobability of disk failures of the one or more disks of the targetstorage system based on the collected diagnostic parameters. The diskfailure indicator(s) can be utilized to predict the probability ofsingle-disk configuration or multi-disk configuration such as a RAIDconfiguration.

As described above, a disk failure indicator can be identified andselected from a set of diagnostic parameters, such as the S.M.A.R.T.attributes or SCISI return codes collected from a set of known faileddisks and working disks, using quantile distribution analysis. Quantilesare points taken at regular intervals from the cumulative distributionfunction of a random variable. Dividing ordered data into q essentiallyequal-sized data subsets is the motivation for q-quantiles; thequantiles are the data values marking the boundaries between consecutivesubsets. Put another way, the k^(th) q-quantile for a random variable isthe value x such that the probability that the random variable will beless than x is at most k/q and the probability that the random variablewill be more than x is at most (q−k)/q=1−(k/q). There are q−1 of theq-quantiles, one for each integer k satisfying 0<k<q.

As described above, in order to predict future disk failures, a diskfailure indicator that can consistently or reliably indicate future diskfailures must be identified. According to one embodiment, certaindiagnostic parameters, which may be selected from at least some of theS.M.A.R.T. attributes, are collected from a set of previously knownfailed disks (e.g., q failed disks) and a set of known working disks(e.g., q working disks). For each of the collected diagnosticparameters, a quantile distribution representation (also referred to asa quantile distribution graph or curve) is generated.

For example, for a set of known working disks (e.g., a predeterminednumber of working disks), values of a particular diagnostic parameterare collected from the set of working disks (for example, via adiagnostic or monitoring tool such as S.M.A.R.T. tool):

-   -   Working Set=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]        In this example, it is assumed there are 10 disks in each set.        The values of the particular diagnostic parameter are stored in        an array and sorted according to a predetermined order, in this        example, an ascending order, from small to large.

Similarly, values of the same diagnostic parameter are collected fromthe set of known failed disks:

-   -   Failed Set=[10, 50, 100, 150, 200, 250, 300, 350, 400, 450]        Similar to the working set, the values of the same diagnostic        parameter for the failed disks are stored in an array and sorted        according to the same order as the working set (e.g., ascending        order).

These two arrays are then plotted against the percentiles of number ofthe disks involved (e.g., 10 disks here) to generate the quantiledistribution representations or graphs for both the known failed disksand known working disks, as shown in FIG. 3. In the example as shown inFIG. 3, a first quantile distribution representation 301 is generatedrepresenting the known working disks while a second quantiledistribution representation 302 is generated representing the knownfailed disks. Referring to FIG. 3, two quantile distributionrepresentations are then analyzed to determine the maximum or mostsignificant difference value between the two quantile distributionrepresentations 301-302. In the example as shown in FIG. 3, the maximumdifference value 303 can be identified at the 100% location, where themaximum difference value is (450−100)=350.

Similar processes are performed for all of the diagnostic parametersthat have been selected as disk failure indicator candidates. Themaximum difference values associated with the diagnostic parameters arecompared with each other. In one embodiment, one of the diagnosticparameter associated with the largest difference value amongst all maybe selected as a disk failure indicator. In one embodiment, a distancebetween two quantile curves may be evaluated by calculating the areasize between the two curves. This takes into account all the points onthe curves rather than one specific point. The parameter that has thebiggest area size between two curve is the best disk failure indicator.

FIG. 4 is a flow diagram illustrating a method for determining a diskfailure indicator according to one embodiment of the invention. Method400 may be performed by processing logic which may include software,hardware, or a combination thereof. For example, method 400 may beperformed by analysis module 153 of FIG. 1. Referring to FIG. 4, atblock 401, processing logic receives diagnostic parameters (e.g.,certain selected S.M.A.R.T. attributes) of a set of known orpredetermined failed disks and working disks associated with one or morestorage systems (e.g., backup storage systems). At block 402, for eachof the diagnostic parameters, processing logic generates a firstquantile distribution representation or graph for the set of workingdisks. At block 403, processing logic generates a second quantiledistribution representation for the set of failed disks. Once all of thequantile distribution representations for all of the diagnosticparameters have been generated, at block 404, the first and secondquantile representations of each diagnostic parameter are compared toselect one or more of the diagnostic parameters as one or more diskfailure indicators. Specifically, for each diagnostic parameter, amaximum or most significant difference value is determined, as describedabove, between the two quantile distribution representations thatrepresent a set of known working disks and a set of known failed disks,respectively. All the maximum difference values of all diagnosticparameters are compared to select the largest difference value amongstall to be a disk failure indicator. Alternatively, multiple diagnosticparameters whose maximum difference values greater than a predeterminedthreshold may be selected as multiple disk failure indicators.

According to one embodiment of the invention, once a disk failureindicator has been selected, the disk failure indicator may be used topredict the disk failure probability of a particular disk, referred toas a target disk of a target storage system. A value of a particulardiagnostic parameter (e.g., predetermined diagnostic parameterrepresenting the determined disk failure indicator) is obtained from atarget disk, for example, using a diagnostic tool such as an S.M.A.R.T.compatible monitoring tool. The value of the predetermined diagnosticparameter is then compared to a predetermined threshold that isassociated the predetermined diagnostic parameter to determine aprobability of disk failures for the target disk. For example, if thevalue of the predetermined diagnostic parameter is greater than thepredetermined threshold, the target disk is considered having a higherrisk of future disk failures; otherwise, the target disk is consideredas a working disk with a lower risk of disk failures. The value that isused to compare the predetermined threshold may be an averaged value ora mathematical representation or function of many values collected fromthe target disk over a predetermined period of time. In one embodiment,the predetermined threshold may be an optimal threshold that isdetermined based on the balances between the accuracy of detectingfuture disk failures and the risk of false positive detection of thetarget disk.

FIG. 5 is a flow diagram illustrating a method for predicting diskfailures of a single disk according to one embodiment of the invention.Method 500 may be performed by processing logic which may includesoftware, hardware, or a combination thereof. For example, method 400may be performed by disk failure predictor 152 of FIG. 1. Referring toFIG. 5, at block 501, processing logic selects a diagnostic parameter asa disk failure indicator based on operating status of a set of knownworking disks and known failed disks. The disk failure indicator may beselected using the techniques as described above. At block 502,processing logic determines an optimal threshold for the diagnosticparameter to categorize whether a particular disk (e.g., a target disk)has a higher risk of disk failures. At block 503, processing logicmonitors or receives values of the selected diagnostic parameter fromone or more disks of a target storage system. For each of the disks, atblock 504, the values of diagnostic parameter of the disks are comparedwith the optimal threshold. At block 505, processing logic categorizes adisk having a higher risk of disk failure if the value of the parameterof that disk is greater than the optimal threshold.

As illustrated above, the optimal threshold may determine the accuracyof the prediction. A too-high or too-low threshold may lead to missingcertain potential failed disks and/or false positive working disks. Inone embodiment, values of a predetermined diagnostic parameter(representing the selected disk failure indicator, such as thereallocated sector count as part of S.M.A.R.T. attributes) are collectedfrom a set of known failed disks and known working disks associated witha target storage system. A first and second quantile distribution graphsof the values of the predetermined diagnostic parameter for the knownfailed disks and known working disks are generated, respectively, inview of a range of values of the known failed disks. An optimalthreshold of the values of the predetermined diagnostic parameter isdetermined by comparing the first and second quantile distributiongraphs. In one embodiment, the optimal threshold is identified at alocation where the difference between the first and second quantiledistribution graphs reaches the maximum. The optimal threshold is thenutilized to indicate or predict whether a particular target disk mayhave a higher probability of disk failure, for example, by comparing thevalue of the predetermined diagnostic obtained from the target diskagainst the optimal threshold.

In one embodiment, all historical failed disks and working disks areanalyzed. The prediction system calculates how many failed disks wouldbe captured by a certain threshold (referred to as an accuracyprediction) and how many working disks would be captured by thisthreshold (referred to as false positive). A first curve or first set ofdata points of the accuracy prediction and a second curve or second setof data points of the false positive as a function of differentthresholds (e.g., threshold candidates) are generated, respectively. Thethreshold which has the biggest difference between the first and secondcurves or data points may be selected as the optimal threshold.

Assuming there are five failed disks with a selected diagnosticparameter (e.g., selected disk failure indicator), in this example RAS,having values of [30, 50, 50, 50, 60] (which may be sorted in apredetermined order such as ascending order). Five working disks withRAS=[10, 20, 30, 40, 40]. In one embodiment, the range of valuesobtained from the known failed disks may be utilized as a set ofthreshold candidates. For example, if the RAS threshold is set to be 30,the prediction algorithm will capture all the disks having RAS>=30 andregard them as impending failures. So it can capture 5 failed disks and3 working disks. So prediction accuracy=5, false positive=3. Likewise,if the threshold is set to be 40, prediction accuracy=4 and falsepositive=2. If the threshold is set to be 50, accuracy=4 and falsepositive=0. If the threshold is set to be 60, accuracy=1 and falsepositive=0, as shown in FIG. 6. From the curves as shown in FIG. 6, thelargest difference in values between the prediction accuracy curve 601and the false positive curve 602 appears at the threshold of 50. As aresult, the threshold of 50 may be selected as the optimal threshold forpredicting disk failures of a single target disk.

FIG. 7 is a flow diagram illustrating a method for determining anoptimal threshold for predicting disk failures according to oneembodiment of the invention. Method 700 may be performed by processinglogic which may include software, hardware, or a combination thereof.For example, method 700 may be performed by disk failure predictor 152of FIG. 1. Referring to FIG. 7, at block 701, processing logic collectsor receives values of a diagnostic parameter associated with a set ofknown failed disks and a set of working disks. The diagnostic parametermay have been selected as a disk failure indicator amongst manydiagnostic parameters such as those listed as part of S.M.A.R.T.attributes or SCISI return codes. The set of failed disks and workingdisks may be associated with a target storage system of which the futuredisk failures are to be predicted. At block 702, processing logicgenerates a first set of data points representing a number of knownfailed disks that would have been captured or identified againstdifferent thresholds or threshold candidates. The range of thethresholds may be determined by the range of the values of thediagnostic parameters of the set of failed disks. At block 703, a secondset of data points is generated, representing a number of the knownworking disks that would have been captured against the differentthresholds or threshold candidates. At block 704, the first set andsecond set of data points are compared in view of the differentthresholds. At block 705, one of the thresholds or threshold candidatesmay be selected as an optimal threshold that has the maximum differencebetween the corresponding values of the first and second sets of datapoints.

According to another aspect of the invention, a selected disk failureindicator may also be used to predict the disk failure probability ofmultiple disks in a RAID configuration. According to one embodiment,values of a predetermined diagnostic parameter (representing theselected disk failure indicator, such as the reallocated sector count aspart of S.M.A.R.T. attributes) are collected from a set of known faileddisks associated with a target storage system. A quantile distributiongraph is generated for the values in view of percentiles (e.g., 10%,20%, . . . , 100%) of a number of known failed disks involved in thequantile distribution. Subsequently, when values of the predetermineddiagnostic parameter are collected from a set of target storage disks,the collected values are applied to the quantile distribution graph todetermine their respective percentiles of the known failed disks. Eachpercentile represents a probability of the corresponding disk failure.The individual disk failure probabilities are then used to calculate theprobability of disk failures of two or more of the target disks.

In one embodiment, values of a predetermined parameter (e.g., selecteddisk failure indicator such as a reallocated sector count) are collectedfrom a set of historically known failed disks. Assuming the number ofRAS value is N, the N values of the predetermined parameter are storedin an array and sorted according to a predetermined order such asascending order from smallest to biggest. A quantile distribution isgenerated based on the sorted numbers. For the purpose of illustratingonly, it is assumed the selected disk failure indicator is RASparameter. In one embodiment, the value of predetermined percentileinterval, in this example, 10 percentile=the RAS value at the [N*0.1]offset of the sorted array. Similarly, the value of 0.X=the RAS value atthe [N*0.x] offset of the sorted array.

For example, it is assumed there are 1000 RAS values corresponding to1000 known failed disks. After sorting of the 1000 RAS values, it isassumed the sorted array contains: 100, 105, 160 . . . (the arraylength=1000). If the value of 105 positioned in the sorted array happensto be 100 (array length*0.1=1000*0.1) the quantile graph or curve at 0.1should be 105. Similarly, if value of 230 positioned in the sorted arrayhappens to be 200 (array length*0.2=1000*0.2), the quintile graph orcurve at 0.2 should be 230. It is assumed that for a particular set ofknown failed disks, its quantile distribution graph is shown in FIG. 8A.

Once the quantile distribution graph has been established, according toone embodiment, it can be used to predict multiple-disk failurescenario, for example, by calculating the probability of individual diskfailures and then determining the probability of two or more disks. Forexample, as shown in FIG. 8A, if a target disk's corresponding parametervalue is around 230, its individual probability of disk failures isabout 50%. Similarly, if a target disk's corresponding parameter valueis around 450, its individual probability of disk failures is about 60%,etc.

For the purpose of illustration, it is assumed there are four diskshaving the predetermined parameter of [11, 30, 110, 240]. By applyingthese numbers into the quantile distribution graph as shown in FIG. 8A,for example, by looking up these numbers in the Y axis of the graph, wecan obtain their respective failure probability numbers from thecorresponding X axis of the graph. In this example, value 11 of the disk1 falls between [0.2, 0.3]; value 30 of disk 2 falls between [0.3, 0.4];value 110 of disk 3 falls between [0.4, 0.5]; and value 240 of disk 4falls between [0.5, 0.6]. According to one embodiment, the probabilitywill be selected as an upper bound of the range (although a lower boundcan also be utilized). As a result, the individual failure probabilitiesof these four disks are [0.3, 0.4, 0.5, 0.6], respectively as shown inFIG. 8B.

From the individual failure probabilities of individual disks, theircorresponding probabilities of working disks can be derived asP(work)=1=P(fail), as shown in FIG. 8B. In a RAID configuration, theRAID group failure can be defined as two or more disk failures in thisexample:P(RAID group failure)=P(disk failurenumber>=2)=1−P(disk_failure_num=0)−P(disk_failure_num=1).P(disk_failure_num=0)=P(disk1_(—) w)*P(disk2_(—) w)*P(disk3_(—)w)*P(disk4_(—) w).P(disk_failure_num=1)=P(disk1_failure)*P(disk2_(—) w)*P(disk3_(—)w)*P(disk4_(—) w)+P(disk1_(—) w)*P(disk2_failure)*P(disk3_(—)w)*P(disk4_(—) w)+P(disk1_(—) w)*P(disk2_(—)w)*P(disk3_failure)*P(disk4_(—) w)+P(disk1_(—) w)*P(disk2_(—)w)*P(disk3_(—) w)*P(disk4_failure).

P(disk failure number=0) refers to the probability of no disk failure.P(disk failure number=1) refers to the probability of one disk failure.P(disk1_w), P(disk2_w), P(disk3_w), and P(disk4_w) refer to theprobabilities of working disk for disk 1 to disk 4, respectively.P(disk1_failure), P(disk2_failure), P(disk3_failure), andP(disk4_failure) refer to the probabilities of disk failure for disk 1to disk 4, respectively. Similarly, the probability of more than anynumber of disks can also be calculated. According to one embodiment,there are two tunable parameters: 1) the number of fail disks to beprevented, where the default number here is >=2 and 2) the number ofdisks in the RAID group (in this example, the number of disks is 3).Both numbers are adjustable based on different requirements and systemsettings.

FIG. 9 is a flow diagram illustrating a method for predicting multipledisk failures according to one embodiment of the invention. Method 900can be performed by processing logic which may include software,hardware, or a combination thereof. For example, method 900 may beperformed by disk failure predictor 152 of FIG. 1. Referring to FIG. 9,at block 901, processing logic builds a quantile distribution graph of adiagnostic parameter (e.g., selected disk failure indicator) from a setof known failed disks. At block 902, processing logic determines diskfailure probability of disks by applying values of the diagnosticparameter collected from the target disks to the quantile distributiongraph. At block 903, the disk failure probability of multiple disks isthen calculated from the individual disk failure probability of theindividual target disks.

As described above according to another embodiment of the invention, inaddition to the straightforward approach that uses the values of certaindiagnostic parameters, a weight vector algorithm is utilized that takesinto account all errors to recognize soon-to-fail disks. The weightvector algorithm evaluates the error degree of every disk fault metric,assigns weights accordingly, and consolidates all weights, for example,using Euclidean length, to present the failure probability. Inparticular, a disk is provided with a weight or score indicatingdifferent error levels instead of only predicting whether the disk willfail. The weight or score enables the system to adjust a failurethreshold by monitoring every disk's score in the same RAID group. Forexample, if there are multiple disks with comparatively high scoresindicating that they may be simultaneously approaching their end oflifespan, the threshold used to categorize whether a particular disk mayfail may be lowered to fail the disks more aggressively, which mayreduce the probability of potential multiple disk failures and improvethe overall system reliability.

Specifically, according to one embodiment of the invention, when a firstand a second quantile distribution representations are created, forexample, by analysis logic such as analysis module 153 of FIG. 1,representing a set of known failed disks and a set of known workingdisks, respectively, a default weight factor or score is assigned toeach of the deciles (or intervals such as percentiles) evenlydistributed over the range represented by the quantile distributionrepresentations. A default weight or score of a particular interval orpercentile represents a degree of potential disk failure of a particulardisk when the value of the diagnostic parameter of that particular diskfalls within the corresponding interval or percentile. Subsequently,when a diagnostic parameter (e.g., reallocated sector count) of a targetstorage disk is received for the purpose of predicting disk failures ofthe target storage disk, disk failure prediction logic, such as diskfailure predictor 152 of FIG. 1, performs a lookup operation on both ofthe first quantile distribution representation (e.g., failed diskquantile distribution) and the second quantile distributionrepresentation (e.g., working disk quantile distribution) correspondingto the diagnostic parameter based on the value of the diagnosticparameter to determine the diagnostic parameter having that particularvalue is represented by both the first and second quantile distributionrepresentations (e.g., whether a quantile distribution contains thevalue of the diagnostic parameter).

In one embodiment, if the value of the diagnostic parameter isrepresented by both the first and second quantile distributionrepresentations (e.g., the particular value of the diagnostic parameterin question is found in both quantile distribution representations),disk failure prediction logic, such as disk failure predictor 152 ofFIG. 1, determines a first weight factor from the first quantiledistribution representation based on the value of the diagnosticparameter and determines a second weight factor from second quantiledistribution representation based on the value of the diagnosticparameter. A third weight factor is then determined based on the firstand second weight factors and a disk failure probability is thendetermined based on the third weight factor. If the value of thediagnostic parameter is only represented by the first quantiledistribution representation (and not by the second quantile distributionrepresentation), the first weight factor is utilized as the third weightfactor.

FIG. 11 is a diagram illustrating a process for predicting disk failuresusing quantile distribution techniques according to one embodiment ofthe invention. The process as shown in FIG. 11 may be performed byprocessing logic which may be disk predictor 152 and/or analysis module153 of FIG. 1. Referring to FIG. 11, it is assumed a quantiledistribution graph for a set of known failed disks 1101 and a quantiledistribution graph for a set of known working disks 1102 for aparticular diagnostic parameter (e.g., RAS) have been created usingcertain techniques described above. In this example, the Y axisrepresents the values of the corresponding diagnostic parameter and theX axis represents the distribution of the values of the diagnosticparameter. The values of the diagnostic parameter may be sorted andevenly distributed in a predetermined number of intervals, which may bein percentiles or deciles. In this example, the values are evenlydistributed in 10 deciles. A decile represents any of the nine valuesthat divide the sorted data into ten equal parts, so that each partrepresents 1/10 of the sample or population).

According to one embodiment, each interval or decile is assigned with aweight, a default weight factor, representing a degree or probability ofdisk failures. In this example, it is assumed the weight assigned toeach decile is 1, 2, . . . , 10 corresponding to deciles 1, 2, . . . ,10, respectively, for the purpose of simplicity; however, other valuesmay be utilized as weights. In this example, the maximum weight is then10 while the minimum weight is 1. The distribution of the weights aspresented in FIG. 11 is also referred to as a weight vector. A higherlevel of error on deciles indicates a higher failure probability. Thediscrimination level between working and failed deciles represents thecorrelation degree between values of the diagnostic parameter and diskfailures.

In one embodiment, for a given value of a diagnostic parameter collectedfrom a target storage disk for the purpose of predicting potential diskfailures, a lookup operation is performed on both the failed diskquantile distribution graph 1101 and the working disk quantiledistribution graph 1102 corresponding to that diagnostic parameter todetermine whether that particular value of the diagnostic parameter isrepresented by both of the quantile distribution graphs 1101 and 1102.If the value falls in an overlapped part of the working and faileddeciles (assuming in the range of P_(th) and Q_(th) deciles of thefailed disks, and M_(th) and N_(th) deciles of working disks), itsweight is (Q−(10−N)). In this example, it is conservatively select anupper bound of the range to account for the possibility of the worstcase situation. (10−N) is the remaining distance towards the end ofworking deciles, representing the survival probability given thatparticular value of the diagnostic parameter. In this example, the valueof 10 represents the maximum default weight given there are 10 decilesin total. The difference between Q and (10−N) determines whether thedisk is more likely to fail or survive. It also takes into account thediscrimination level between working and failed deciles.

Note that in some cases (Q−(10−N)) may result in a negative number,which indicates the disk with that particular value of the diagnosticparameter is more probable to survive than to fail. In such case, theweight is assigned with a predetermined minimum weight such as zero,indicating that such a value has no contribution to the finalprediction. According to one embodiment, if the value of the diagnosticparameter is only represented by the failed disk quantile distributiongraph (e.g., the working quantile distribution graph does not containthat particular value of the diagnostic parameter), the default weightcorresponding to the decile that the value falls within is used as theweight assigned to that particular diagnostic parameter of the targetstorage disk for the purpose of determining the probability of diskfailures. Such scenario means there is no working disk with this value.

Referring back to FIG. 11, it is assumed that in one scenario the valueof a given diagnostic parameter is represented by dash line 1112. Basedon value 1112 of the diagnostic parameter, a lookup operation isperformed on both failed disk quantile distribution graph 1101 andworking disk quantile distribution graph 1102 to determine whether value1112 is represented by both quantile distribution graphs 1101-1102. Inthis example, it is assumed value 1112 is found at point 1121 ofquantile distribution graph 1101. Based on point 1121, its X axis value(e.g., decile) is determined, in this example, between 2 and 3 (e.g.,P=2, Q=3). Similarly, it is assumed value 1112 can be found at point1122 of quantile distribution graph 1102, and its X axis value isdetermined, in this example, between 8 and 9 (e.g., M=8, N=9). Based onthe formula described above, the weight assigned to value 1112 will beQ−(10−N)=2. The probability of disk failures for the value 1112 will bedetermined based on weight of 2 in this example.

In another scenario, it is assumed that the value of the parameter isrepresented by dash line 1111. Based on value 1111 of the diagnosticparameter, a lookup operation is performed on both failed disk quantiledistribution graph 1101 and working disk quantile distribution graph1102 to determine whether value 1111 is represented by both quantiledistribution graphs 1101-1102. In this example, only quantiledistribution graph 1101 contains value 1111 at point 1123. Therefore,since point 1123 falls between 8 and 9, the weight assigned to value1111 will be 9 (e.g., Q=9).

Therefore, for a given value of a diagnostic parameter, the weightassigned to that value of the diagnostic parameter is (a first weightgiven obtained from the failed disk quantile distribution graph−(themaximum weight−a second weight obtained from the working disk quantiledistribution graph)). If the calculated weight is negative, apredetermined minimum weight (e.g., zero) is assigned to that value ofthe diagnostic parameter.

FIG. 12 is a flow diagram illustrating a method for predicting diskfailures according to one embodiment of the invention. Method 1200 maybe performed by processing logic which may include software, hardware,or a combination thereof, such as, for example, disk predictor 152and/or analysis module 153 of FIG. 1. Referring to FIG. 12, at block1201, for a given diagnostic parameter, a first quantile distributiongraph is generated based on values of the diagnostic parameter that arecollected from a set of known failed disks. A second quantiledistribution graph is generated based on values of the diagnosticparameter that are collected from a set of known working disks. At block1202, processing logic assigns a default weight or score to each of theintervals or deciles of the quantile distribution, representing a degreeof potential disk failures. Subsequently at block 1203, when a value ofthe corresponding diagnostic parameter is received for the purpose ofpredicting potential disk failure of a target disk, a weight isdetermined based on a first weight obtained from the first quantiledistribution graph and a second weight obtained from the second quantiledistribution graph based on the value of the diagnostic parameter (e.g.,Q−(10−N)). At block 1204, a probability of disk failures of the targetdisk is determined based on the weight that is derived from the firstand second weights obtained from both quantile distribution graphs(e.g., based on calculated Euclidean length).

FIG. 13 is a flow diagram illustrating a method for predicting diskfailures according to another embodiment of the invention. Method 1300may be performed by processing logic which may include software,hardware, or a combination thereof, such as, for example, disk predictor152 and/or analysis module 153 of FIG. 1. Referring to FIG. 13, at block1301, in response to a diagnostic parameter collected from a targetstorage disk for the purpose of predicting disk failures, processinglogic determines whether a value of the diagnostic parameter isrepresented by a first quantile distribution graph and a second quantiledistribution graph generated based on a set of known failed disks and aset of known working disks, respectively. That is, processing logicdetermines whether the value of the received diagnostic parameter iscontained in both the quantile distribution graphs. If so, at block1302, processing logic calculates a weight factor for the receiveddiagnostic parameter based on a first weight obtained from the firstquantile distribution graph and a second weight obtained from the secondquantile distribution graph, for example, using the formula (e.g.,Q−(10−N)) described above. Thereafter, the probability of potential diskfailures is determined based on the calculated weight factor at block1304. On the other hand, if the value of the received diagnosticparameter is only represented by the first quantile distribution graph,at block 1303, the weight obtained from the first quantile distributiongraph based on the value of the received diagnostic parameter is used todetermine the probability of potential disk failures at block 1304.

The above weight assignment process results in a weight vector. Eachelement of the weight vector reflects an error degree of that particularfault type and represents its corresponding failure probability fromthat error prospective. A variety of different methods can be used todetermine a probability of potential disk failures. In one embodiment, adisk failure probability can be determined by consolidating all weightstogether dependent upon the understanding of relationships amongst thosefaults.

According to one embodiment, a Euclidean length (also referred to asEuclidean distance) is utilized to combine all the weights because ittakes into account all the possibilities and works more efficient. Inmathematics, the Euclidean distance or Euclidean metric is the“ordinary” distance between two points that one would measure with aruler, and is given by the Pythagorean formula. By using this formula asdistance, Euclidean space (or even any inner product space) becomes ametric space. Given an input weight vector of [p1, p2, . . . pn], theEuclidean length=square root (p1^(^2)+p2^(^2)+ . . . +pn^(^2)). Forexample, if the weight vector=[3, 4, 2], the Euclidean length=squareroot (3*3+4*4+2*2)=square root (29)=5.3. For a given set of values of aparticular diagnostic parameter collected from a particular disk, theweight vector used to predict disk failures of that particular disk isgenerated using the techniques described above, dependent upon whetherthere is an overlapped area between a failed disk quantile distributiongraph and a working disk quantile distribution graph. Thereafter, theEuclidean length of the weight vector is calculated for that disk andthe calculated Euclidean length is compared to a predetermined failurethreshold.

If the Euclidean length of the disk weight vector exceeds apredetermined failure threshold T, the disk in question is considered tobe a soon-to-fail disk. The two quantile distribution graphs representthe tradeoff between the true positive number, which includes impendingdisk failures captured by the above formula(s) and the false alarmamount, which includes working disks identified incorrectly. Bycomparing these two quantile distribution representations over theentire range of the identification threshold(s), one can take intoaccount all possible cost-based scenarios in terms of the tradeoffbetween missing impending disk failures versus failing working onesincorrectly.

FIG. 10 is a block diagram illustrating a deduplication storage systemaccording to one embodiment of the invention. For example, deduplicationstorage system 1000 may be implemented as part of a deduplicationstorage system as described above, such as, for example, thededuplication storage system as shown in FIG. 1. In one embodiment,storage system 1000 may represent a file server (e.g., an appliance usedto provide network attached storage (NAS) capability), a block-basedstorage server (e.g., used to provide SAN capability), a unified storagedevice (e.g., one which combines NAS and SAN capabilities), a nearlinestorage device, a direct attached storage (DAS) device, a tape backupdevice, or essentially any other type of data storage device. Storagesystem 1000 may have a distributed architecture, or all of itscomponents may be integrated into a single unit. Storage system 1000 maybe implemented as part of an archive and/or backup system such as adeduplicating storage system available from EMC® Corporation ofHopkinton, Mass.

In one embodiment, storage system 1000 includes a deduplication engine1001 interfacing one or more clients 1014 with one or more storage units1010 storing metadata 1016 and data objects 1018. Clients 1014 may beany kinds of clients, such as, for example, a client application, backupsoftware, or a garbage collector, located locally or remotely over anetwork. A network may be any type of networks such as a local areanetwork (LAN), a wide area network (WAN) such as the Internet, acorporate intranet, a metropolitan area network (MAN), a storage areanetwork (SAN), a bus, or a combination thereof, wired and/or wireless.

Storage devices or units 1010 may be implemented locally (e.g., singlenode operating environment) or remotely (e.g., multi-node operatingenvironment) via an interconnect, which may be a bus and/or a network.In one embodiment, one of storage units 1010 operates as an activestorage to receive and store external or fresh user data, while theanother one of storage units 1010 operates as a target storage unit toperiodically archive data from the active storage unit according to anarchiving policy or scheme. Storage units 1010 may be, for example,conventional magnetic disks, optical disks such as CD-ROM or DVD basedstorage, magnetic tape storage, magneto-optical (MO) storage media,solid state disks, flash memory based devices, or any other type ofnon-volatile storage devices suitable for storing large volumes of data.Storage units 1010 may also be combinations of such devices. In the caseof disk storage media, the storage units 1010 may be organized into oneor more volumes of redundant array of inexpensive disks (RAID). Datastored in the storage units may be stored in a compressed form (e.g.,lossless compression: HUFFMAN coding, LEMPEL-ZIV WELCH coding; deltaencoding: a reference to a chunk plus a difference; etc.). In oneembodiment, different storage units may use different compressionmethods (e.g., main or active storage unit from other storage units, onestorage unit from another storage unit, etc.).

The metadata, such as metadata 1016, may be stored in at least some ofstorage units 1010, such that files can be accessed independent ofanother storage unit. Metadata of each storage unit includes enoughinformation to provide access to the files it contains. In oneembodiment, metadata may include fingerprints contained within dataobjects 1018, where a data object may represent a data chunk (alsoreferred to as a data segment), a compression region (CR) of one or moredata chunks, or a container of one or more CRs. Fingerprints are mappedto a particular data object via metadata 1016, enabling the system toidentify the location of the data object containing a chunk representedby a particular fingerprint. When an active storage unit fails, metadatacontained in another storage unit may be utilized to recover the activestorage unit. When one storage unit is unavailable (e.g., the storageunit has failed, or is being upgraded, etc.), the system remains up toprovide access to any file not stored in the failed storage unit. When afile is deleted, the metadata associated with the files in the system isupdated to reflect that the file has been deleted.

In one embodiment, the metadata information includes a file name, astorage unit identifier identifying a storage unit in which the chunksassociated with the file name are stored, reconstruction information forthe file using the chunks, and any other appropriate metadatainformation. In one embodiment, a copy of the metadata is stored on astorage unit for files stored on a storage unit so that files that arestored on the storage unit can be accessed using only the informationstored on the storage unit. In one embodiment, a main set of metadatainformation can be reconstructed by using information of other storageunits associated with the storage system in the event that the mainmetadata is lost, corrupted, damaged, etc. Metadata for a storage unitcan be reconstructed using metadata information stored on a main storageunit or other storage unit (e.g., replica storage unit). Metadatainformation further includes index information (e.g., locationinformation for chunks in storage units, identifying specific dataobjects).

In one embodiment, deduplication storage engine 1001 includes fileservice interface 1002, segmenter 1004, duplicate eliminator 1006, filesystem control 1008, and storage unit interface 1012. Deduplicationstorage engine 1001 receives a file or files (or data item(s)) via fileservice interface 1002, which may be part of a file system namespace1020 of a file system associated with the deduplication storage engine1001. The file system namespace 1020 refers to the way files areidentified and organized in the system. An example is to organize thefiles hierarchically into directories or folders, which may be managedby directory manager 1022. File service interface 1012 supports avariety of protocols, including a network file system (NFS), a commonInternet file system (CIFS), and a virtual tape library interface (VTL),etc.

The file(s) is/are processed by segmenter 1004 and file system control1008. Segmenter 1004, also referred to as a content store, breaks thefile(s) into variable-length chunks based on a variety of rules orconsiderations. For example, the file(s) may be broken into chunks byidentifying chunk boundaries using a content-based technique (e.g., afunction is calculated at various locations of a file, when the functionis equal to a value or when the value is a minimum, a maximum, or othervalue relative to other function values calculated for the file), anon-content-based technique (e.g., based on size of the chunk), or anyother appropriate technique. In one embodiment, a chunk is restricted toa minimum and/or maximum length, to a minimum or maximum number ofchunks per file, or any other appropriate limitation.

In one embodiment, file system control 1008, also referred to as a filesystem manager, processes information to indicate the chunk(s)association with a file. In some embodiments, a list of fingerprints isused to indicate chunk(s) associated with a file. File system control1008 passes chunk association information (e.g., representative datasuch as a fingerprint) to index 1024. Index 1024 is used to locatestored chunks in storage units 1010 via storage unit interface 1012.Duplicate eliminator 1006, also referred to as a segment store,identifies whether a newly received chunk has already been stored instorage units 1010. In the event that a chunk has already been stored instorage unit(s), a reference to the previously stored chunk is stored,for example, in a chunk tree associated with the file, instead ofstoring the newly received chunk. A chunk tree of a file may include oneor more nodes and each node represents or references one of thededuplicated chunks stored in storage units 1010 that make up the file.Chunks are then packed by a container manager (which may be implementedas part of storage unit interface 1012) into one or more storagecontainers stored in storage units 1010. The deduplicated chunks may befurther compressed into one or more CRs using a variation of compressionalgorithms, such as a Lempel-Ziv algorithm before being stored. Acontainer may contains one or more CRs and each CR may contain one ormore deduplicated chunks (also referred to deduplicated segments). Acontainer may further contain the metadata such as fingerprints, type ofthe data chunks, etc. that are associated with the data chunks storedtherein.

When a file is to be retrieved, file service interface 1002 isconfigured to communicate with file system control 1008 to identifyappropriate chunks stored in storage units 1010 via storage unitinterface 1012. Storage unit interface 1012 may be implemented as partof a container manager. File system control 1008 communicates (e.g., viasegmenter 1004) with index 1024 to locate appropriate chunks stored instorage units via storage unit interface 1012. Appropriate chunks areretrieved from the associated containers via the container manager andare used to construct the requested file. The file is provided viainterface 1002 in response to the request. In one embodiment, filesystem control 1008 utilizes a tree (e.g., a chunk tree obtained fromnamespace 1020) of content-based identifiers (e.g., fingerprints) toassociate a file with data chunks and their locations in storageunit(s). In the event that a chunk associated with a given file or filechanges, the content-based identifiers will change and the changes willripple from the bottom to the top of the tree associated with the fileefficiently since the appropriate content-based identifiers are easilyidentified using the tree structure. Note that some or all of thecomponents as shown as part of deduplication engine 1001 may beimplemented in software, hardware, or a combination thereof. Forexample, deduplication engine 1001 may be implemented in a form ofexecutable instructions that can be stored in a machine-readable storagemedium, where the instructions can be executed in a memory by aprocessor.

In one embodiment, storage system 1000 may be used as a tier of storagein a storage hierarchy that comprises other tiers of storage. One ormore tiers of storage in this hierarchy may utilize different kinds ofstorage devices and/or may be optimized for different characteristicssuch as random update performance. Files are periodically moved amongthe tiers based on data management policies to achieve a cost-effectivematch to the current storage requirements of the files. For example, afile may initially be stored in a tier of storage that offers highperformance for reads and writes. As the file ages, it may be moved intoa tier of storage according to one embodiment of the invention. Invarious embodiments, tiers include different storage technologies (e.g.,tape, hard drives, semiconductor-based memories, optical drives, etc.),different locations (e.g., local computer storage, local networkstorage, remote network storage, distributed storage, cloud storage,archive storage, vault storage, etc.), or any other appropriate storagefor a tiered data storage system.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the invention also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present invention are not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the invention as described herein.

In the foregoing specification, embodiments of the invention have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the invention as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method of predicting diskfailures, the method comprising: receiving, by a processor, a diagnosticparameter of a target storage disk of a storage system having aplurality of storage disks; determining, by the processor, a firstweight factor from a first quantile distribution representation based ona value of the diagnostic parameter, wherein the first quantiledistribution representation represents a quantile distribution of valuesof the diagnostic parameter of a set of known failed disks; determining,by the processor, a second weight factor from a second quantiledistribution representation based on the value of the diagnosticparameter, wherein the second quantile distribution representationrepresents a quantile distribution of values of the diagnostic parameterof a set of known working disks; calculating, by the processor, a thirdweight factor for the diagnostic parameter for the target storage diskbased on the first weight factor and the second weight factor; anddetermining, by the processor, a probability of potential disk failureof the target storage disk based on the third weight factor.
 2. Themethod of claim 1, wherein the diagnostic parameter is one ofreallocated sector count, medium error, timeout, pending sector count,uncorrectable sector count, connection error, and data error of thetarget storage disk.
 3. The method of claim 1, further comprising: priorto receiving the diagnostic parameter of the target storage disk,collecting values of the diagnostic parameter from the set of faileddisks and the set of working disks; generating the first and secondquantile distribution representations based on the collected values ofthe diagnostic parameter in a predetermined number of deciles; andassigning a default weight factor to each of the decile, the defaultweight factor representing a degree of disk failure of the correspondingdecile.
 4. The method of claim 3, wherein determining a first weightfactor from a first quantile distribution representation comprises:performing a lookup operation on the first quantile distributionrepresentation based on the value of the diagnostic parameter;determining a first decile based on the value of the diagnosticparameter; and determining the first weight factor based on a firstdefault weight factor assigned to the first decile.
 5. The method ofclaim 4, wherein determining a second weight factor from a secondquantile distribution representation comprises: performing a lookupoperation on the second quantile distribution representation based onthe value of the diagnostic parameter; determining a second decile basedon the value of the diagnostic parameter; and determining the secondweight factor based on a second default weight factor assigned to thesecond decile.
 6. The method of claim 5, wherein calculating a thirdweight factor comprises: calculating a first difference value between amaximum weight factor and the second weight factor; and computing thethird weight factor based on a difference between the first weightfactor and the first difference value.
 7. The method of claim 6, furthercomprising assigning the third weight factor with a predeterminedminimum weight factor if the calculated third weight factor is below apredetermined threshold.
 8. The method of claim 6, further comprisingassigning the first weight factor to the third weight factor if thesecond weight factor cannot be determined from the second quantilerepresentation based on the value of the diagnostic parameter.
 9. Anon-transitory machine-readable medium having instructions storedtherein, which when executed by a processor, cause the processor toperform operations of predicting disk failures, the operationscomprising: receiving a diagnostic parameter of a target storage disk ofa storage system having a plurality of storage disks; determining afirst weight factor from a first quantile distribution representationbased on a value of the diagnostic parameter, wherein the first quantiledistribution representation represents a quantile distribution of valuesof the diagnostic parameter of a set of known failed disks; determininga second weight factor from a second quantile distributionrepresentation based on the value of the diagnostic parameter, whereinthe second quantile distribution representation represents a quantiledistribution of values of the diagnostic parameter of a set of knownworking disks; calculating a third weight factor for the diagnosticparameter for the target storage disk based on the first weight factorand the second weight factor; and determining a probability of potentialdisk failure of the target storage disk based on the third weightfactor.
 10. The non-transitory machine-readable medium of claim 9,wherein the diagnostic parameter is one of reallocated sector count,medium error, timeout, pending sector count, uncorrectable sector count,connection error, and data error of the target storage disk.
 11. Thenon-transitory machine-readable medium of claim 9, wherein theoperations further comprise: prior to receiving the diagnostic parameterof the target storage disk, collecting values of the diagnosticparameter from the set of failed disks and the set of working disks;generating the first and second quantile distribution representationsbased on the collected values of the diagnostic parameter in apredetermined number of deciles; and assigning a default weight factorto each of the decile, the default weight factor representing a degreeof disk failure of the corresponding decile.
 12. The non-transitorymachine-readable medium of claim 11, wherein determining a first weightfactor from a first quantile distribution representation comprises:performing a lookup operation on the first quantile distributionrepresentation based on the value of the diagnostic parameter;determining a first decile based on the value of the diagnosticparameter; and determining the first weight factor based on a firstdefault weight factor assigned to the first decile.
 13. Thenon-transitory machine-readable medium of claim 12, wherein determininga second weight factor from a second quantile distributionrepresentation comprises: performing a lookup operation on the secondquantile distribution representation based on the value of thediagnostic parameter; determining a second decile based on the value ofthe diagnostic parameter; and determining the second weight factor basedon a second default weight factor assigned to the second decile.
 14. Thenon-transitory machine-readable medium of claim 13, wherein calculatinga third weight factor comprises: calculating a first difference valuebetween a maximum weight factor and the second weight factor; andcomputing the third weight factor based on a difference between thefirst weight factor and the first difference value.
 15. Thenon-transitory machine-readable medium of claim 14, wherein theoperations further comprise assigning the third weight factor with apredetermined minimum weight factor if the calculated third weightfactor is below a predetermined threshold.
 16. The non-transitorymachine-readable medium of claim 14, wherein the operations furthercomprise assigning the first weight factor to the third weight factor ifthe second weight factor cannot be determined from the second quantilerepresentation based on the value of the diagnostic parameter.
 17. Adata processing system, comprising: a processor; and a memory storinginstructions, which when executed from the memory, cause the processorto perform operations, the operations including receiving a diagnosticparameter of a target storage disk of a storage system having aplurality of storage disks, determining a first weight factor from afirst quantile distribution representation based on a value of thediagnostic parameter, wherein the first quantile distributionrepresentation represents a quantile distribution of values of thediagnostic parameter of a set of known failed disks, determining asecond weight factor from a second quantile distribution representationbased on the value of the diagnostic parameter, wherein the secondquantile distribution representation represents a quantile distributionof values of the diagnostic parameter of a set of known working disks,calculating a third weight factor for the diagnostic parameter for thetarget storage disk based on the first weight factor and the secondweight factor, and determining a probability of potential disk failureof the target storage disk based on the third weight factor.
 18. Thesystem of claim 17, wherein the diagnostic parameter is one ofreallocated sector count, medium error, timeout, pending sector count,uncorrectable sector count, connection error, and data error of thetarget storage disk.
 19. The system of claim 17, wherein the operationsfurther comprise: prior to receiving the diagnostic parameter of thetarget storage disk, collecting values of the diagnostic parameter fromthe set of failed disks and the set of working disks; generating thefirst and second quantile distribution representations based on thecollected values of the diagnostic parameter in a predetermined numberof deciles; and assigning a default weight factor to each of the decile,the default weight factor representing a degree of disk failure of thecorresponding decile.
 20. The system of claim 19, wherein determining afirst weight factor from a first quantile distribution representationcomprises: performing a lookup operation on the first quantiledistribution representation based on the value of the diagnosticparameter; determining a first decile based on the value of thediagnostic parameter; and determining the first weight factor based on afirst default weight factor assigned to the first decile.
 21. The systemof claim 20, wherein determining a second weight factor from a secondquantile distribution representation comprises: performing a lookupoperation on the second quantile distribution representation based onthe value of the diagnostic parameter; determining a second decile basedon the value of the diagnostic parameter; and determining the secondweight factor based on a second default weight factor assigned to thesecond decile.
 22. The system of claim 21, wherein calculating a thirdweight factor comprises: calculating a first difference value between amaximum weight factor and the second weight factor; and computing thethird weight factor based on a difference between the first weightfactor and the first difference value.
 23. The system of claim 22,wherein the operations further comprise assigning the third weightfactor with a predetermined minimum weight factor if the calculatedthird weight factor is below a predetermined threshold.
 24. The systemof claim 22, wherein the operations further comprise assigning the firstweight factor to the third weight factor if the second weight factorcannot be determined from the second quantile representation based onthe value of the diagnostic parameter.